Multi-task feature learning-based improved supervised descent method for facial landmark detection
Multi-task feature learning-based improved supervised descent method for facial landmark detection
复制标题
基于多任务特征学习的改进监督下降法进行人脸特征点检测
DOI：
10.1007/s11760-017-1125-4
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Yi Jin
中科院分区：
文献类型：
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作者：
Peng Bian;Zhengnan Xie;Yi Jin
人脸特征检测在人脸验证、面部表情识别、年龄估计等人脸理解任务中发挥着重要作用，而模型初始化和特征提取是监督式人脸特征检测的关键。在现有的方法中，由检测器误差和初始化不一致引起的不匹配是很常见的。为了解决这一问题，我们提出了一种基于多任务特征学习的改进监督下降法（MtFL-iSDM）的鲁棒面部地标定位方法。在该方法中，首先进行快速检测，对眼睛和嘴巴进行定位，并根据快速的人脸点检测，对初始化模型进行适应。其次，在改进的监督下降法模型上采用多任务特征学习，以获得更好的性能。在四个基准数据库上的实验表明，我们的方法达到了最先进的性能。
Facial landmark detection has played an important role in many face understanding tasks, such as face verification, facial expression recognition, age estimationet al.Model initialization and feature extraction are crucial in supervised landmark detection. Mismatching caused by detector error and discrepant initialization is very common in these existing methods. To solve this problem, we have proposed a new method called multi-task feature learning-based improved supervised descent method (MtFL-iSDM) for the robust facial landmark localization. In this new method, firstly, a fast detection will be processed to locate the eyes and mouth, and the initialization model will adapt to the real location according to fast facial points detection. Secondly, multi-task feature learning is adopted on our improved supervised descent method model to achieve a better performance. Experiments on four benchmark databases show that our method achieves state-of-the-art performance.